System and method for aggregating and sharing accumulated information

ABSTRACT

An accumulated information data store may include topic nodes, each having a text description of limited length and (in some cases) one or more attributes. A particular topic node may be associated as a parent topic node or a child topic node such that the topic nodes form at least one data tree. An information processing engine may access information in the accumulated information data store and determine that a plurality of topic node text descriptions are similar and classify them as similar topic nodes. At least a part of the text description associated with one of the similar topic nodes may be selected as a favorable text description for the similar topic nodes. The system may also unify the similar topic nodes as identical topic nodes when they are currently grouped together as having the same upper tree hierarchy.

The present application is a continuation of U.S. patent applicationSer. No. 15/438,063 entitled “SYSTEM AND METHOD FOR AGGREGATING ANDSHARING ACCUMULATED INFORMATION” and filed Feb. 21, 2017 (allowed) whichclaimed the benefit of U.S. Provisional Patent Application No.62/297,963 entitled “SYSTEM AND METHOD FOR AGGREGATING AND FOR SHARINGDATA BASED ON CROWD ACCUMULATED WISDOM” filed on Feb. 22, 2016 and ofU.S. Provisional Patent Application No. 62/298,478 entitled “SYSTEM ANDMETHOD FOR AGGREGATING AND FOR SHARING DATA BASED ON CROWD ACCUMULATEDWISDOM” filed on Feb. 23, 2016. The entire contents of thoseapplications are incorporated herein by reference.

BACKGROUND Background

The Internet can facilitate an exchange of information betweenindividuals and groups of individuals. For many people, the Internetrepresents a principal way by which they gather information from otherpeople about many different topics. Moreover, people may use theInternet to share their own information and opinions with other people.The result may be a complex array of intersecting and non-intersectinginformation about a substantial number of topics between vast numbers ofindividuals. Navigating and viewing such a large, inter-related set ofinformation can be a difficult, confusing, and time-consuming task.

It would therefore be desirable to aggregate and share accumulatedinformation in an automatic and accurate manner.

SUMMARY

According to some embodiments, an accumulated information data store mayinclude a plurality of topic nodes, each topic node having a textdescription of limited length and at least some of the topic nodes beingassociated with one or more attributes. In some cases, a particulartopic node may be associated as a parent topic node to one or more otherchild topic nodes such that the topic nodes in the accumulatedinformation data store form at least one data tree. An informationprocessing engine may access information in the accumulated informationdata store and determine that a plurality of topic node textdescriptions are similar and classifying them as similar topic nodes. Atleast part of the text description associated with one of the similartopic nodes may be selected as a favorable text description for thesimilar topic nodes. The system may automatically identify all othertopic nodes in the accumulated information data store that have the sametext description as one of the similar topic nodes and merge all of theother identified topic nodes with the similar topic nodes, wherein thesimilar topic nodes and all of the other identified topic nodes are nowclassified as similar to each other and retain their associatedattributes. The system may also unify the similar topic nodes asidentical topic nodes when they are currently grouped together as havingthe same upper tree hierarchy. The system may classify the unified topicnodes that are also similar topic nodes as a single topic noderepresented by the favorable text description, wherein any attributeassociated with the unified topic nodes is automatically mathematicallycombined.

Some embodiments comprise: means for storing, in an accumulatedinformation data store, a plurality of topic nodes, each topic nodehaving a text description of limited length and at least some of thetopic nodes being associated with one or more attributes, wherein aparticular topic node is associated as a parent topic node to one ormore other child topic nodes such that the topic nodes in theaccumulated information data store form at least one data tree; meansfor accessing, by an information processing engine, information in theaccumulated information data store; means for determining, by theinformation processing engine, that a plurality of topic node textdescriptions are similar and classifying them as similar topic nodes;means for selecting at least part of the text description associatedwith one of the similar topic nodes as a favorable text description forthe similar topic nodes; means for automatically identifying all othertopic nodes in the accumulated information data store that have the sametext description as one of the similar topic nodes and merging all ofthe other identified topic nodes with the similar topic nodes, whereinthe similar topic nodes and all of the other identified topic nodes arenow classified as similar to each other and retain their associatedattributes; means for unifying the similar topic nodes as identicaltopic nodes when they are currently grouped together as having the sameupper tree hierarchy; and means for classifying the unified topic nodesthat are also similar topic nodes as a single topic node represented bythe favorable text description, wherein any attribute associated withthe unified topic nodes is automatically mathematically combined.

Some technical advantages of some embodiments disclosed herein areimproved systems and methods to aggregate and share accumulatedinformation in an automatic and accurate manner.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A and 1B show data structures for implementing a system toaggregate and share accumulated information in accordance with someembodiments.

FIG. 2 is a method according to some embodiments.

FIG. 3 illustrates two spheres of knowledge hierarchically arranged inaccordance with some embodiments.

FIG. 4 is an example of merging similar topic nodes according to someembodiments.

FIG. 5 is an example of unifying identical topic nodes in accordancewith some embodiments.

FIG. 6 is another example of merging similar topic nodes according tosome embodiments.

FIG. 7 is a block diagram of a system to aggregate and share data basedon crowd accumulated wisdom in accordance with some embodiments.

FIG. 8 is a display to view data and declare similarity between topicsaccording to some embodiments.

FIG. 9 is a display to enable a user to categorize and classify data inaccordance with some embodiments.

FIG. 10 shows an illustrative plurality of categories to be rated forimportance (by a user using a scale of values) and inserted to thesystem as a listing according to some embodiments.

FIGS. 11A and 11B show an illustrative embodiment of a personal diarybelonging to a specific topic, under a specific hierarchy, in accordancewith some embodiments.

FIG. 12 illustrates a comprehensive topic's details exhibition accordingto some embodiments.

FIG. 13 illustrates a category or sub-topic being updated or inserted inaccordance with some embodiments.

FIG. 14 illustrates a topic being selected for viewing or updating (or atopic being newly entered) according to some embodiments.

FIG. 15 illustrates a process to fill data (listing) associated with aspecific topic in accordance with some embodiments.

FIG. 16 is a high level view of a system architecture according to someembodiments.

FIG. 17 is a method according to some embodiments.

FIG. 18 is a block diagram of an apparatus or platform in accordancewith some embodiments of the present invention.

FIG. 19 is a tabular portion of a monitoring node database according tosome embodiments.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth in order to provide a thorough understanding of embodiments.However it will be understood by those of ordinary skill in the art thatthe embodiments may be practiced without these specific details. Inother instances, well-known methods, procedures, components and circuitshave not been described in detail so as not to obscure the embodiments.

Some embodiments described herein provide anonymous and/or identifiedusers' with a dynamic system for aggregating and presenting information.The information may be continuously collected from users and may beorganized in brief textual topics. Moreover, associated data may becollected and stored as child topics. As used herein, the term “topic”may refer to, for example, a node in a tree that represents a certaintextual data and that may (or might not) have child topics or nodes. Achild of a topic may also include textual data which is related to thetext of the parent topic, and these texts may be inserted by a user. Achild of a topic may also function as a topic itself, and have otherrelated child topics, creating a branched tree.

According to some embodiments, the system is divided into one or morespheres of knowledge and/or groups of users, each with their ownrestrictions and/or preloaded data, in a hierarchy-based arrangement ofthe spheres and groups. A user may influence the information in aninteractive way, such as by editing, removing, and/or amending topics. Auser may also classify information by attaching or linking text togetherindicating that the topics are similar or identical in the system. Auser may also interactively rate which text he or she agrees with, andwhich text is the most important for a topic. This rate value may bepart of a description fields' values attached to each topic. An examplefor this layout maybe a sphere of “Health” including the topic “Hand”and below it a child topic “Elbow” and below that a line of childrenuntil the final topic is: “Radial head fracture”→“Actions”→“Exercisingphysiotherapy.” The last child may have a rating of “Agreed and veryimportant” and a description value of “Avg. repetition=Every 5 hours.”According to some embodiments, a user may create his or her own diaryfor a topic when writing in data. The diary may include, for example,child topics and free text. The diary may include data that is insertedand updated by the user at different times, or in different stages, asdifferent records. Utilizing these updates in the diary for calculationspurposes might make the exhibited data in the system more reliable. Theoperation of identifying similar topics may be performed after comparingseveral topics in the same or different hierarchy levels of the trees,relating to the meaning of their text without relating to the context(parent topics). If the meaning of the text is similar then one of thetopics' text also represents the other topics, for presentationpurposes, and it is identified as the favorable topic. The favorabletopic may then be displayed when presenting the data (instead of theother topics' text).

For example, topics with the text “the power is in our hands,” “thepower relies in our hand,” and “we have the power” may be similar, whilea topic “the power of the hand” is not similar to them. The rules forsimilarity may be plural/singular, male/female, etc. The rules fornon-similar topics might be associated with different text meaning,maybe because of new data being added or being more specific. Searchingmay be performed according to text that is input by the user (or inother ways) and may take into account results of searches in similartopics. Thus, a search of a certain text can result in topics withanother text that has a similar meaning. A search may also take intoaccount the text of parent topics in the hierarchy tree line.

A “unifying” operation for identical topics, as opposed to the mergingoperation for similar text, may unify topics in which the combination oftheir texts and the texts that is generated from the hierarchy (line ofparent topics) of each respective topic have equal meaning. For example:a child topic “advantages” under the hierarchy of “competiveness” whichis child of “characteristic of sport” at the head of the tree line,might be unified with the topic “advantages of competiveness in sport”at the head of the tree line, because they are identical topics. Inorder for the unifying action to happen upon similar topics, there needsto be identical parents. If the parent is not the same parent, then theparents should be unified. In other cases, a similar topics' line shouldbe similar in every respective node until one common parent (or severalunified parents) is found or until the first parent topics in each ofthe hierarchy lines is reached. The unifying operation may cause anautomatic averaging of their rating values. The averaging may, forexample, generate a new set of averaged values for each topic of theunified topics along with its own values. Their child topics may also becoupled—thus providing the option, when viewing a certain unified topicas the main presented topic, to review the coupled child topics of allof its' unified topics. Child topics that are now coupled (and weresimilar previously to the coupling operation) may become unified, andtheir child topics may also become coupled. The same rule would applyfor them until the bottom of each tree line is reached.

The unifying and the identification of similar topics may provide threelevels for viewing and averaging the data: (1) viewing a specificpresented topic, which has one specific hierarchy above it, and theunique data linked to it, meaning only its own child topics; (2) viewingthe level of unified averaged presented topic-meaning all of the coupledchild topics of the topics that are unified with the presented topic;and (3) viewing the level of similar topics to the presented topic whichwill include the child topics of both the levels above, and also thecoupling with child topics of the similar topics to the presented topic.

The viewing and averaging of the data as result of the unifying may alsotake into account the hierarchically arranged topics and groups andspheres of information. Each topic may be displayed and averaged withthe unified topics that are included in the same or lower (morespecific) group or sphere level. The lower level may be a group or asphere wherein the “General” sphere is at the top position and includesaveraging and displaying of all the spheres/groups below it, calculatingall the values of its lower more specific layers. For example, if a userbelongs to a certain group he or she will see only its data and the dataof the lower groups. If a user selects a certain sphere/group he or shewill see its data and the data of spheres/groups within it only (whichare at a lower level to it).

Consider, for example, the sub-trees:

-   -   Generic→Health→XXX Hospital    -   Generic→High-Tech group        In this case, topics that are fed to the “XXX hospital” are part        of the “Health” and of “Generic” including their children. The        topics may be changed as part of “Generic” but these changes        will be averaged within “Generic” alone. More specific        sphere/group such as “Health” might not be affected.

According to some embodiments, spheres may be affected by default in theratings but not in favorable text. Groups might not be affected bydefault in ratings, but only if the user decides that he or she wantsthis action to happen. Thus, changes in a higher sphere, such as thegeneral sphere, may affect the lower, more specific, sphere in somecases (and will not affect the lower sphere in other cases, depending onuser selection). Note that a first sphere might, or might not, shareinformation with a second sphere.

When viewing children of a topic there may be an option to view the mostdirectly related topics and the least related topics. For example thetopic “Leg” (which is within the sphere “Health”) may include the childtopic “Knee” which includes its child topic “Broken knee” which includesa child topic “Broken knee treatment.” The results of the presentationof “Leg” may be child topics of “Leg” including “Knee,” or “Knee” and“Broken knee,” or “Knee,” “Broken knee,” “Broken knee treatment.”

Choosing the furthest related level for the presented topic may resultin presentation of a lot of child topics of different relation level tothe presented topic. In case similar topics exist among those childtopics, they may be unified and averaged temporarily for the currentpresentation. This procedure may occur in any situation when similar,but not identical, child topics are presented or calculated together.Such an approach may thus make the system, in this enlarged state,temporarily more concise and less repetitive (but potentially alsotemporarily less credible) based on the user's preference.

By default, the presentation and calculation of child topics may beprovided according to a summary of the favorable unified topic's directchildren for the current sphere or group. To change the defaultcalculation and presentation, the parameters that are used may include:an identical topics selection field, a groups and spheres selectionfield, and/or a hierarchy relation selection field. The system mayaggregate all of the information for each topic, showing the mostrelevant child topics first with their rate of importance, calculatingthese results in various ways that may include, for example: Joining orexcluding different spheres or groups; and/or calculating weight ofcertain topics according to rates of users that are associated withthese topics. Such rates might be associated with, for example, a levelof importance, a popularity, and/or a contemporary date. Other types ofcalculations might include filtering the presentation of the dataaccording to personal filters; allowing the identifying of similartopics by the user or by the system and selecting of only one topic fromthe similar topics such that only the selected topic is displayed tousers; and/or unifying data of two or more topics that have similarhierarchies (parent topics) and may be considered identical; and/orcombining child topics that are at a lower level tree branch of thetopic as being direct child topics of the topic. The system may thusaggregate data and allow the summarizing of specific knowledge of aspecific topic (and allow a user to share his or her knowledge knowingthat the knowledge is summarized and filtered by other users). Theaggregation may, according to some embodiments, take into account aloneor more of the following parameters: Joining or excluding differentspheres or groups; calculating weight of certain topics according torates of users that are associated with these topics; such rates may belevel of importance, popularity, or contemporary date, some or alltogether; filtering by personal data of the user; identifying similartopics and presenting them as one topic; averaging values of identicaltopics; combining child topics that are at a lower level tree branch ofthe topic as being direct child topics of the topic.

Thus, embodiments may provide an engine that presents to a user datathat is more relevant to his or her preferences while reducing therepetition of information while still summarizing relevant information.

FIGS. 1A and 1B show exemplary data structures for implementing a systemaccording to some embodiments. An electronic record or table 110 isconfigured for storing data related to a topic. The term “topic” mayrefer to, for example, a node in a tree that includes text representinga certain topic. A topic may be a child of another topic and a topic mayhave children. The topics enable easy navigation and also assist insearching the relevant data by identifying two or more similar topics.The topics may be created by the user or may be predefined by thesystem. The topic table 110 includes a parent field 111, a childrenfield 112, a text code field 113, and a unified nodes field 114. Theparent field 111 includes a link to the parent of the topic in the treethat represents the data. The parent may be another topic. If the topic110 is the head of the tree, then the parent field 111 is empty. Thechildren field 112 includes a list of the children of the topic 110.These children are topics themselves. If the topic 110 has no children,the list is empty. The text code field 113 includes code from a texttable 120 that represents the text of this topic. The unified nodesfield 114 includes list of links to other topics that have been selectedby users of the system as unified or have been identified by the systemas unified. For example, unified nodes may be associated with situationswhere all of the texts of the topics comprising the specific topicrepresented in table 110 (meaning the parent's line) appear in theunified topic's hierarchies (meaning the ones comprising each of theirparent lines) in one way or another preserving the same whole meaning(without omitting, adding, or changing any significant data). Theunified topics may be identical to the represented topic in terms of thetextual meaning as a whole, taking into account the affiliation derivingfrom the topic's parent line (in other words, the hierarchy as a wholeis identical, but not essentially in exactly the same order). Inpresentation, the level of unified causes the presentation of all of theunified topics' children and respectively their summarized calculatedvalues, as if they were the represented topic's children. For example,assume topic A with parent B and above it parent C and above it Z,wherein B is a topic with text b and C is a topic with text c. Nowassume topic W with parent X and above it Y and above it Z, wherein X isa topic with text that has now been identified as similar to b and Y isa topic with text that has now been identified as similar to c. Nodes Cand Y may become unified because those nodes have the same parent, andnodes B and W become unified because those nodes have unified parents.The child topics A and W may become coupled and may be shown togetherwhen presenting topic B (or can be selected as unified topics if theyhave similar text since they have similar hierarchies). If the unifiednodes field 114 is not empty, then the system may display all of thechildren of the unified nodes 114 in addition to the children of topic110 and vice versa. Such unifying enables the system to summarize to theuser all the data related to a certain topic. A table 120 is configuredfor storing texts and associated codes. The table 120 includes a codefield 121, a text field 122 and a favorable text code 123. The textfield 122 includes a text that is associated with the code field 121.The text may be entered by a user or may be predefined. A specific textis associated with a single code and cannot be associated with more thanone code. The favorable text code 123 includes a link to the favorabletopic's text. This text has been identified by the user or the system assimilar to the text of topic 110 in field 122 and has been chosen by theuser or the system to be displayed instead of the text in field 122.

If the favorable text code 123 of the topic 110 is not empty, the systemmay display the text of the similar topic which was set as favorableinstead of the text of this topic. Every text description that wasidentified as similar to another text description is kept in table 120,creating a dictionary of similar texts in the system, so that in anycase other topics in the system have the same texts syntax as the onesdefined as similar in the dictionary, they become automatically similartopics. Other dictionary comparisons may be based on combinations ofsimilar texts, in a way that parts of the text of a topic is defined assimilar in the dictionary to parts of the text of another topic toautomatically identify similarity between topics. This dictionarycomparisons process applies also to future topics in the system, and allexisting topics in the system. Note that specific information spheres,such as a user viewing his or her own diary (or specific spheres ofknowledge and groups of people) might keep their own text similaritiesdictionaries without being affected by outside favorable text in thesystem. If the favorable text code field 123 of the topic 110 is empty,the system displays the text of the topic 110. If the favorable textcode field 123 and the code field 121 are the same then topic 110 is thefavorable topic of its similar topics. A record table 130 is configuredfor recording the operation of a specific user on a specific session.The session may be identified by a topic and by date. The record table130 includes a children values field 131, a personal details field 132,a story field 133 and a date field 134. The children values field 131includes a link to a list of children of this topic from a table 150(see FIG. 1B) that are given a rate value by the user in this sessionand also the values of the children's description fields. The userpersonal details field 132 includes a link to a list of details aboutthe user that are relevant to the session. The details may include name,religion, gender, etc.

The story field 133 includes free text that may be entered by the userand is related to this record. The date field 134 includes the date andtime that this record was entered by the user. A diary table 140 may beconfigured for handling diary of a specific user with regard to his orher activities in the system related to a specific topic. The diarytable 140 includes a topic field 141, a records field 142 and a sphereor group field 143. The topic field 141 includes a link to the topic towhich this diary and all of its records refer to. A records field 142includes a link to the list of records. The structure of the records isdefined in the record table 130. The sphere or group field 143 includesthe sphere or group to which this diary belongs. Examples of spheres are“Medical,” “Sport,” and “Art.” The group may be a group of people. Thechildren values table 150 is configured for storing the child topics andrates and the description fields' values of the child topics that arebeing filled by the user in a record. The table 150 includes a topicfield 151, a rate field 152, and description fields' field 153. Thetopic field 151 includes a link to the child topic that is filled inwith values by the user in relation to its parent topic 141. The ratefield 152 includes the rate value which indicates how much the useragrees and values the text of the child topic 151 in relation with theparent topic 151. The description fields' field 153 includes a link to alist of description codes and their values that are used for averagingthe description fields. The codes are connected to a certain descriptionfield type such as “repetition,” and the value has a semanticattachment, such as “1=once, 2=every day, 3=twice a day, 4=all thetime,” etc.

According to some embodiments, when a topic is presented each user mayrate the child topics which may include specific description fields'values according to relevancy. The rating may be used for displayingonly topics having the highest rates to the user. In one embodiment, thesystem averages all of the ratings of the child topics of a certaintopic regardless of their description fields. This may be calculated,for example, considering all of the records of a specific diary as oneaveraged rate for each child topic. The system may then average per eachchild topic a total average of all the users' diaries' averages. Therate for a child topic may be determined according to the total averageand the number of diaries belonging to its parent topic. In otherembodiments, different description fields' values and records dates maybe taken into consideration when calculating the rates and presentingthe most relevant child topic for a certain topic with its own specificvalues and not the averaged values of all the appearances.

For example, FIG. 2 illustrates a method of searching a topic,displaying data, and/or entering data that might be performed by some orall of the elements of the system described with respect to FIG. 1. Theflow charts described herein do not imply a fixed order to the steps,and embodiments of the present invention may be practiced in any orderthat is practicable. Note that any of the methods described herein maybe performed by hardware, software, or any combination of theseapproaches. For example, a computer-readable storage medium may storethereon instructions that when executed by a machine result inperformance according to any of the embodiments described herein.

At S210, a user may enter text in a searching screen in order to searcha topic for displaying or updating. At S212, the system may exhibit aset of topics for the searched text according to a search priorityalgorithm. According to some embodiments, the search priority algorithmmay consider giving more weigh to combinations of: topics from the samegroup of people that includes the user; topics from a certain pre-loadedsphere (which may be strict in its topic trees and therefore should bemore dependable and well organized); topics form a “Generic” spherewhich contains all data, but is dynamically built and unorganized;topics which have sematic text that resembles the semantics of thesearched text; topics which were defined as similar in the system to thesearched text; topics with a combination of first part of their textthat resembles part of the searched text and the second part of theirtext which is defined as similar in the system to the remaining text ofthe searched text; topics that with the combination of their text andthe text of their line of parent topics resembles the searched text oris defined as similar to it (or part of it) in the system; and/or topicsthat have more popularity, higher rating, contemporary date, previoususers' selections for this searched text etc.

At S214, the user may point out similarity between exhibited topics. Ifthe user pointed out similarity, then the system learns the suggestionof similarity, and decides later on if to define the topics as similarbased on statistical comparisons. If so, the text of one of them willrepresent the others. For example, when exhibiting two similar childtopics of one topic only one of them may be shown with the favorabletext between them both. Note that the system may record the informationprovided by the user in S214. For example, the system may later use thatinformation to make a suggestion to the user (e.g., based on his or herpast selections) or to other users+.

If a selection of topic is chosen by the user at S216, he or she choosesone of the topics that were exhibited at S218. If a fill-in of data ischosen by the user at S220, the system may then exhibit the child topicsof the chosen topic at S226. At S228, the user may point out similaritybetween the child topics which are connected to the topic he or sheselected (and the system may learn the similarity). Note that the systemmight record the information provided by the user in S218 and/or S228.For example, the system may later use that information to make asuggestion to the user (e.g., based on his or her past selections) or toother users (e.g., if many users make the same selection, it mightappear higher in a list of ranked choices). The user may fill in his orher rating and other deception field values that are connected to thechild topics at S232. The user may also insert a new child topic for thechosen topic at S234 (that is, the system may learn about appropriateratings, values, and new child topics) and the process may end at S236.

If a presentation of data is chosen by the user at S220, the system maythen exhibit the child topics of the chosen topic at S227. At S229, theuser may point out similarity between the child topics which areconnected to the topic he or she selected (and the system may learn thesimilarity). Note that the system may record the information provided bythe user in S229. For example, the system may later use that informationto make a suggestion to the user (e.g., based on his or her pastselections) or to other users.

If the user selects to enter a new topic at S216, he or she enters a newtopic into the system at S238. In this case, there are no child topicsto be exhibited, but instead new child topics are inserted by the user,with ratings and description fields' values. After the new child topicsare inserted with ratings and description fields at S240. The system maythen learn about appropriate ratings, values, and new child topics, andthe process may then end at S236.

FIG. 3 illustrates 300 two spheres of knowledge hierarchically arrangedin accordance with some embodiments. In particular, a generic sphere 310may contain all of the information while a sport sphere 320 mayrepresent a sphere of information within the generic sphere 310. Thesport sphere 320 may comprise a hierarchy of topic nodes arranged intoone or more data trees like the tree 322. In particular, the sportsphere 320 includes the following topic nodes and associated textdescriptions (and, where applicable, rating score):

-   -   A—“Characteristic of sport”    -   AB—“Competiveness”    -   ABC—“Advantages”    -   ABCX—“I think it makes me more ambitious in real life”        (rating=3)    -   ABCZ—“I feel my physical fitness has improved” (rating=4)    -   ABD—“Disadvantages”    -   AC—“I lose weight easier when I exercise sport 2 times every        week” (rating=5; description field—intensity=“extreme”;        description field—workout=“30 minutes”)    -   ACE—“I think it makes me more ambitious in real life” (rating=1)

FIG. 3 further includes another tree hierarchy 330 within the genericsphere 310 containing the following topic nodes and associated textdescriptions (and, where applicable, rating score):

-   -   Z—“Advantages of competiveness in sport”    -   ZB—“It makes people more ambitious in real life” (rating=5)    -   ZC—“Loss of weight is achieved easier with sport practiced 4        times per week” (rating=3; description field—intensity=“low”;        description field—workout=“60 minutes”)        The system may manipulate the multiple trees 322, 330, keeping        them in their original settings but also connecting between them        when appropriate, for summarization and aggregation purposes.

Note that a topic node might be associated with a “rating” and/or a“description” (or several descriptions). As used herein, the term“rating” might refer to a numerical value that indicates a relativeimportance of a topic node (e.g., a value from 1 through 5). The term“description” or “description fields” might include other informationthat describes something about the topic node. For example, a topic nodemight include an “Intensity” description with the following possiblelevels: “low,” “medium,” or “extreme.” According to some embodiments,the levels within a description might be combined or averaged in somesituations. For example, a description of “low” and a description of“extreme” might be averaged to a description of “medium.”

In the example of FIG. 3, the two trees 322, 330 have nodes A and Z atthe tree tops. Consider now a user who wants to connect tree nodes(topics) in a presentation of data. Initially, a current sphere of thesystem may be set to is “Generic,” and so the two trees 322, 330 areshown as if they were on the same level of group/sphere (that is, theuser can't tell the difference between them).

FIG. 4 is an example 400 of merging similar topic nodes according tosome embodiments. In particular, some users (or an automatic process)may point out a similarity (for a merge action) between ABCX and ZB, asillustrated by the bold line labeled “˜” in FIG. 4. The system may, forexample, decide if these two nodes are truly similar using statisticalcomparisons. If the two nodes are declared similar, then a favorable orrepresentative text is selected between them. In this example, assumethat the text of ZB has been selected as being the favorable one. Thesystem which is set to the “Generic” sphere will now present them bothwith the same text of ZB while their respective rating values (and otherdescription fields values if they exist) remain the same: (ABCX˜ZB)(with the underlined node label representing the favorable text). Thus,when the system presents (or displays) the connected nodes whileexhibiting topic “ABC” Table I will be provided:

TABLE I Main presented Topic ABC Advantages Rating Child topics ABCX Itmakes people more 3 represented ambitious in real life by the text of ZBABCZ I feel my physical 4 fitness has improved

FIG. 5 is an example 500 of unifying identical topic nodes in accordancewith some embodiments. In this example, some users (or an automaticprocess) point out that Z and ABC are identical (unification action) asillustrated by the bold line labeled “=” in FIG. 5. The system maydecide, for example, if the two nodes should truly be unified asidentical using statistical comparisons. If the two nodes are declaredas unified, then they are unified. As a result, if they are categories(that is, a child topic with a rating) then their ratings are averagedtogether, and if they have description fields they are also averaged:(ABC=Z). As a result of this unification, the children of these topicsare coupled and the children that were similar before the unificationprocess also become unified. Thus, in this example, ABCX and ZB becomeunified as illustrated by the label “Similar (˜), Now Equal (=)” in FIG.5. Note that this process may also occur upon the children of ABCX andZB who were similar, until the bottom of each tree is reached:

ABCX˜ZB+ABC=Z→(ABCX˜=ZB)

-   -   A not˜ Z, AB not˜ Z, ABC not˜ Z (because of missing semantic        data between them)        Thus, when the system presents (or displays) the connected nodes        while exhibiting topic “Z” Table II will be provided:

TABLE II Advantages of Main presented competiveness in Description TopicZ sport Rating fields Child topics ZB It makes people 4 (averaged moreambitious rating with in real life ABCX) ABCZ I feel my physical 4fitness has improved ZC Loss of weight is 3 intensity = achieved easier“low” with sport practiced workout = 4 time per week “60 minutes”And after the last unification when exhibiting topic ABC, Table III willbe provided:

TABLE III Main presented Description Topic ABC Advantages Rating fieldsChild topics ABCX It makes people 4 represented more ambitious by thetext in real life of ZB, now averaged with ZB. ABCZ I feel my physical 4fitness has improved ZC Loss of weight 3 intensity = is achieved easier“low” with sport practiced workout = 4 times per week “60 minutes”

According to some embodiments, a dynamic presentation and aggregation ofinformation may be provided through three selection fields (sometimesreferred to as “constriction fields”). For example, by decision of theuser or the system, a presentation of the main presented topic maycontain an enlarged view of child topics beyond the default rule of“direct children of all the unified topics with the main presentedtopic, in the current group/sphere.” Note that each time similar topicsare presented as child topics of a topic they will be summarizedtemporarily for the presentation. For example, FIG. 6 is another example600 of merging similar topic nodes according to some embodiments. Asbefore, (ABCX˜=ZB) and (ABC=Z). In this example, another similarity wasnoticed: (AC˜ZC) as illustrated by the bold line labeled “˜” betweenthose two nodes in FIG. 6 and also ABCX is automatically identified assimilar to ACE because of text similarity (to the point of being exactlythe same text). As a result, ACE˜ABCX˜=ZB.

According to some embodiments, an identical topics selection field(sometimes referred to as a “singularity selection field”) may be usedto present data in three different ways: (1) “Specific Presented Topic”;(2) “Identical Topics” (which might be the default setting); and (3)“Similar Topics.”

In the case of “Specific Presented Topic” (sometime referred to as“Unique Topic”), when the main presented topic is node Z, the childrenpresented will be: ZB (rated without ABCX=5) and ZC (rated=3;intensity=“low,” workout=“60 minutes).

In the case of “Identical Topics” (sometimes referred to as “UnifiedAveraged Presented Topic”), when the main presented topic is node Z, thechildren presented will be: ZB (with an average rated with ABCX=4), ABCZ(rated=4), and ZC (rated=3; intensity=“low,” workout=“60 minutes”)

In the case of “Similar Topics” (sometimes referred to as “Equivalent ByText Only”), when the main presented topic is node Z, the childrenpresented will be: ZB (with an average rated with ABCX=4), ABCZ(rated=4), and ZC (rated=3, intensity=“low,” workout=“60 minutes). Whenthe main presented topic is node ZC, the children presented will be: ACEand ZCE. Furthermore, if ACE˜ZCE, then the children presented will be:ACE (with an average rated with ACE and ZCE—similar nodes becomesummarized temporarily).

According to some embodiments, information may be presented inaccordance with a hierarchy relation selection field in three differentways: (1) “Directly Related Topics” (which might be the defaultsetting); (2) “Close Subordinates”; and (3) “Least Related Topics.”

In the case of “Directly Related Topics” (sometimes referred to as“Direct Subordinates,” “direct relation”), when the main presented topicis A, the children presented will be: AB, AC (represented by the text ofZC and the rating and description field values of AC).

In the case of “Close Subordinates” (sometimes referred to as “closerelation”), a level of presentation may depend on the length and size ofthe tree, and show only a limited amount of child hierarchy under thepresented topic. For example, if the main presented topic is A, thechildren presented will be: AB, AC (represented by the text of ZC andthe rating and description field values of AC, ACE (represented by thetext of ZC and the rating of ACE), ABD, and ABC.

In the case of “Least Related Topics” (sometimes referred to as “DistantLevels,” “distant relation”), when the main presented topic is A, thenTable IV will be provided:

TABLE IV Main presented Characteristic Description Topic A of SportRating fields Child Topics AB Competiveness ABD Disadvantages ABCAdvantages ABCZ I feel my physical 4 fitness has improved ABCX It makespeople 3 represented more ambitious by the text of in real life ZB,averaged with ZB and ACE. ZBC (child of Z = ABC) ZBD (child of Z = ABC)ZCE (child of Z = ABC) AC Loss of weight 4 intensity = (represented isachieved easier “moderate” by the text with sport workout = of ZC. avgpracticed 3 times “45 rate and avg per week minutes” description fieldswith ZC because children of Z are added) ACEY

According to some embodiments, information may be provided in accordancewith a group and sphere selection field which are arrangedhierarchically, a change in this setting will change the default of“Generic” sphere 310 which existed in this example until now.

In the case of “Generic” sphere 310 (the top level presentation of alldata), when the main presented topic is ABC, the children presented willbe: ABCX (represented by the text of ZB, and average rated with ZB=4),ABCZ (rated=4), and ZC (rated=3, intensity=“low,” workout=“60 minutes”).

In the case of “Sport” sphere 320 (the more specific lower level), whenthe main presented topic is ABC, Table V will be provided:

TABLE V Main presented Topic ABC Advantages Rating Child Topics ABCX Itmakes people more 3 represented ambitious in real life by the text ofZB. without avg with ZB. ABCZ I feel my physical 4 fitness has improvedNote that favorable text might not cross spheres/groups according tosome embodiments, but is only one illustrative approach. According toother embodiments, no text from a higher level would show up in a lowermore specific level. An opposite role might apply for averaged ratingwhich might be averaged between different spheres/groups according tosome embodiments.

According to some embodiments, different selection fields/constrictionsmay be combined. Note that usually the system may be set to defaultsettings associated with presentation of the main presented topic andchild topics. These might be, for example, direct children of theunified topics within the current sphere/group. To get a largerperspective on a certain main presented topic, these settings can bechanged to allow for the combination of changes to default settings inseveral selection fields/constrictions, making the multi-tree alignmentact more like a graph (that is, flattening the hierarchy related to themain presented topic and connecting different trees by similarity). Forexample, when the main presented topic is ZC, the following settings:Sphere=Generic, Hierarchy=Least Related Topic, and IdenticalTopics=Similar Topics would result in Table VI being provided:

TABLE VI Loss of weight is achieved easier with sport practiced Mainpresented Topic ZC 4 times per week Rating Child topics ZCE ACE (this Itmakes people more 1 is a child of ambitious in real life AC whichtemporarily became summarized with ZC, represented by the text of ZB andthe rating of ACE) ACEY (Least Related Topics child of ZC)

As will now be described, a system may be divided into one or morespheres of knowledge and/or groups of users, each with their ownrestrictions and/or preloaded data, in a hierarchy-based arrangement ofthe spheres and groups. Users may influence the information in aninteractive way, such as by linking similar text together, interactivelyrating text they agree with (or disagree with), and/or indicating whichtopics are most important. A user may create his or her own diary for atopic when entering data, thus enabling modifying and updating ofvarious categories. The system may aggregate all of the information foreach topic, showing the most relevant categories first with their rateof importance, and calculate results in various ways, such as:

-   -   joining or excluding different spheres or groups,    -   calculating weight of certain categories according to rates of        users that are associated with these categories (such rates may        be level of importance, popularity, and/or contemporary date),    -   filtering the presentation of the data according to personal        filters,    -   allowing identification of similar topics or categories by the        user or the system and the selecting of only one topic or        category from the similar topics or similar categories such that        only the selected favorable topic or category is displayed to        users (if certain text is identified as similar to another text,        the result of searching for the certain text may also show the        text that is identified as similar), and/or    -   joining or excluding the topic's own remotely related categories        belonging to different levels of the same topic's extended        hierarchy.        In this way, the system may provide an engine that presents to a        user data that is more relevant to his or preferences while        eliminating repetition of information and summarizing relevant        information.

Various exemplary embodiments may represent systems and methods for thecollecting, updating, rating, classifying, extraction, selection,searching, sorting, filtering, aggregating, displaying, and/orpresenting of data in a variety of formats and media. Note that thetextual information may be related to various topics collected from asubstantial number of users, or specified groups of individuals, in thecontext of brief textual topics and categories layout, based on theknowledge of many people. The knowledge of the people may be calculatedgiving weight to certain terms that may include, for example:

-   -   Joining or excluding different spheres of knowledge that may be        defined in advance with preloaded data in their hierarchy tree        of knowledge/topics. Such an approach may take into account that        every sphere may also be subjected to specific restrictions to        the collection of data procedures (by ways that may include        restrictions on new inserts, updates and rates). Examples may        include no new topics, an only categories' rating mode, constant        description fields etc. Spheres may also have no preloaded data,        and/or no restrictions.    -   Joining or excluding different groups of individuals and their        accumulated knowledge (authentication may be required).        According to some embodiments, specific restrictions to the        collection of data may be applied, similar to the spheres.    -   These spheres and groups and their exclusive associated data may        be laid on a hierarchy based scale enabling the user to choose        which level he would prefer the system to be temporarily        adjusted for. For example, the level titled “Generic” (or the        similar) means the general sphere, containing all the data from        all the other spheres and groups.    -   Giving more weight to certain categories, and consequently        priority in presentation, may be achieved via attributes that        include user rates of importance for a topic, popularity by way        of total rates count, contemporary date, etc.    -   Filters that consist of personal details related to a certain        state, such as age and gender by default. Note that embodiments        might use other characteristics according to the sphere or group        that the listing is associated with (e.g., recovery level and        therapy type for a health sphere of knowledge).    -   Joining or excluding similar topics and/or categories, changing        them from singular to multiple, thus increasing or decreasing        categories amount for a topic, calculation rates for the        categories themselves, and other calculations.    -   These singularity levels may be on a scale enabling a user to        choose which level is of interest, after selecting a topic for        various actions. The singularity levels might include, for        example:        -   i. A singular “Specific Presented Topic” (or a similar            phrase) and its categories, that were filled under its            unique text and specific hierarchy.        -   ii. A unified and summarized “Identical Topics” (or a            similar phrase) level which means basically all of the            topics comprising the specific topic, requiring the same            specific hierarchy (not omitting or adding or changing any            important data). In presentation, these are identical to the            main topic presented in terms of the textual meaning as a            whole, taking into account the affiliation (in other words,            the hierarchy as a whole is identical, but not essentially            in exactly the same order). This level may cause the            presentation of all of the identical topics' categories and            respectively their summarized calculated values, as if they            were the main topic's categories.        -   iii. A merged “Similar and close” (or a similar phrase),            which is a reduced version of the next level titled “Similar            topics,” joining only topics that have identical/similar or            the same unique topic as one of their close parent topics in            their hierarchies.        -   iv. A merged “Similar topics” level (or similar phrase),            means joining topics that have similar text (not omitting or            adding or changing any important data) but are not similar            neither by affiliation nor hierarchy, and as a result, not            the same specific topic. In presentation, this level can            cause a large amount of categories to be presented and            calculated, that aren't necessarily connected between them.    -   Joining or excluding the topic's own remotely related categories        belonging to different levels of the same topic's extended        hierarchy, subordinates of subordinates, etc.    -   These hierarchy relation levels are on a scale enabling the user        to choose a level of interest (e.g., for the Directly Related        Topics, meaning direct categories, or close subordinates,        meaning the first few close levels to be calculated and        presented as if they were Directly Related Topics, or Least        Related Topics).    -   In all of the three level scales (spheres/groups, identical        topics, hierarchy relation) one side of the scale serves as the        more specific and bounded level while the other side serves as        the more general level containing all of the data in the more        specific levels. These may determine the general constrictions        according to which the system is temporarily adjusted,        influencing all of the data aggregated and displayed.        Further embodiments may be used for collecting, updating,        rating, classifying, extraction, selection, searching, sorting,        filtering, aggregating, displaying, and/or presenting data in a        variety of formats and media the textual information related to        various topics collected from a substantial number of users.        This knowledge of the users may be calculated giving weight to        certain terms elected by the user that correlates to each of the        terms specified above.

FIG. 7 shows a block diagram system 700 for aggregating and sharing databased on accumulated knowledge in accordance with some embodiments ofthe disclosed subject matter. In particular, the system 700 may be usedby one or more identified or anonymous individuals to extract knowledge,referred to as “readers” using reader devices 760 (e.g., computers,smartphones, etc.). The system 700 may also be used by one or moreidentified or anonymous users to fill-in or enter data, referred to as“writers” using writer devices 750 (e.g., computers, smartphones, etc.).These people 750, 760 are interactively networked via theInternet/Intranet 740 to one or more web servers 730 that are coupled toone or more databases 710. The system 700 may also include an optionalcache memory 720 or similar feature between the one or more web servers730 and the one or more databases 710.

FIG. 8 is a display 800 to provide data to a user according to someembodiments. The display 800 includes a topic's hierarchy 810, a sphereinput area 820, a topic area 830, and a constrictions area 840.According to some embodiments, the following guidelines may beassociated with the display:

-   -   When one or more of the three selection fields appears, in there        is an option for the user to choose which level along these        fields he wants the system to be temporarily adjusted for,        influencing mostly the amount of categories displayed for the        topic and their values. The combination of the three fields        forms a constriction rule.    -   The constriction rule may be passed through the system's        different embodiments while the same user is using the system.    -   If one of the selection fields is absent from the user's        control, then the system uses a default value for that field        which is determined in advance.    -   The categories that are presented and calculated for a topic are        the ones that contain the data that is related to the main topic        at the head of the page, and the sub-topic at the head of the        card index (if a card index or a similar presentation form        exist), this data is also confide to the specific hierarchy of        the two topics, described in the “Hierarchy Field.”    -   The most relevant categories are shown first with their        associated summed up description fields' and importance values,        they have priority which is determined after the aggregation        process calculates how much weight to give them.    -   When a “Filters” section is present, there is an option for the        reader to filter the categories displayed, deciding whose        information he wants to view as declared by the writers,        influencing the amount and values of the topic's categories.    -   The user may apply several actions on every topic/sub        topic/category exhibited, by selecting the button “!” (or the        equivalent) next to every topic/sub topic/category, a list of        actions is exhibited and the user can choose between several        actions that may include:        -   a. Merging—attaching two or more topics/categories together            that have similar text, so that in the future the system may            unite these topics and use only one of them for            presentation. The one that is elected to be representative            is called the favorable one, and the system may present a            separate screen for collecting the user's choice of the            favorable one, or relay on the order of the            categories/topics election to decide the user's favorable            choice.        -   b. Declaring a harmful/non legal text.        -   c. Details—navigates to view the extended details of the            topic/category.    -   Several actions that users may perform, and especially readers'        actions on writers' data, won't take effect immediately; they        may be accumulated instead, for future analysis by the system.        The system decides later on when and if their changes will be        performed, these actions may include: merging, unifying,        changing listings' topic, etc.    -   Any Topic or Category exhibited in a multiple layout        presentation in the system, which has similar topics' or        categories' text is usually represented by one of them with the        most favorable text.    -   The system's default setting for calculating and summarizing        categories' values or amount of categories for a specific topic        is the “Identical Topic” level of the selection field “identical        topics”. This means that topics that are considered as the same        specific topic with the same specific hierarchy (identical as a        whole) in the system are calculated and presented together.    -   Topics and categories in the system can be unified if they are        identical, or merged if they are similar by their text (not        considering affiliation/hierarchy). As a result a few roles        derive:        -   a. Unified topics are not necessarily merged, and merged            topics are not necessarily unified.        -   b. Unified topics are summarized by default.        -   c. Merged topics are summarized by default only if they are            merged along the whole chain of their parent topics reaching            the top of the chain, or the first unified topics or the            same specific presented topic.        -   d. When topics are summarized, then their subordinate topics            or categories that are merged are also summarized, this            occurs along the whole descending hierarchy.    -   When multiple categories or topics are displayed, there is an        option for the user to perform a search, sort actions.    -   Where text is to be entered, any or all information that would        have otherwise been provided by the user, may also be provided        automatically or semi-automatically based on the information        already stored in the database and/or cache memory as described        in connection with the embodiment shown in FIG. 7.    -   Validation checks on new entered text within topics or        categories fields, may include an only lexicon words spelling        check (lexicon might be updated according to contemporary        demands), harmful words check, harmful and illegal sentences.        These and other checks are all subject to the specific sphere or        group restrictions.

FIG. 8 is a display 800 to enable a user to view data and declaresimilarity between topics in accordance with some embodiments. Inparticular, the display 800 includes an illustrative plurality offiltered categories containing data related to a topic. The user cannavigate along the hierarchy tree of topics and choose a different topicfor viewing. He or she may apply a general constriction to the data bynavigating between different spheres/groups. The user may also navigatebetween the different subtopics at the top of the card index, and choosewhich sub topic he wants to view. The information exhibited to thereader may reside in the database and/or cache memory as illustrated inFIG. 7.

With respect to FIG. 8, upon the user viewing a topic, sub topics andtheir data, that may be constricted to a certain group/sphere and/orfilters. The user may also perform actions like selecting a button (orthe equivalent) titled “Sub Topics” enabling him to choose which subtopics he wants to view instead of, or additionally to the currentlyexhibited popular sub topics. Another action is selecting a button (orthe equivalent) titled “Add Listing” that presents him with an exemplaryembodiment in which he can insert a new listing, based on thetopic/subtopics and categories which are currently exhibited, as will beshown in FIG. 10. Another action that the user may perform is byselecting a button (or the equivalent) titled “Show All/Arrange/Print”that presents him with the exemplary data editor which contains multiplecategories in a cramped layout for various reasons such as printing,classifying and arranging, as will be shown in FIG. 9. Another actionthat the user may perform is by selecting the button (or the equivalent)titled “Details,” then he or she will be presented with thecomprehensive topic's details exhibition as illustrated in FIG. 12.

FIG. 9 is a display 900 with an illustrative plurality of categories tobe rated for importance (by a user via a scale of values) and insertedto the system as a listing according to some embodiments. The display900 may be associated with a data editor, which encapsulates classifyingand presenting multiple categories in a cramped layout, for one or moretopics (three topics example is shown in the display 900). Average ratesof importance for a category are represented by small symbols ornumbers, or something similar. The user may apply filters, search, sortand general constrictions to the data, using the three selectionfields—spheres/groups, identical topics levels, and hierarchy relationlevels. The user is able to apply different classifying, arranging andediting actions on the categories and topics by ways of one by one, andby ways of some or all together by selecting the proximate to thecategories checkbox fields, or by a similar way. While inserting orupdating a writer's specific listing, the exemplary embodiment shown inFIG. 9 enters another mode that may include an option to fill in ratesand description fields' values to the categories, by temporarilypresenting a separate screen over the illustration for each category(may be triggered by selecting the “!” button) or by enlarging the spacefor each category within the illustration, or by other ways. Uponinserting or updating, the order of the categories presented in FIG. 9is changed giving more weight to the user's own filled out categories.His or her categories are presented first and may be highlighted withdifferent colors; optional colors are orange and green (e.g., orange mayrepresent partial agreement). This value may be addressed in furtherembodiments prompting the user to type in his or her own text or chooseanother category about which the user can fully (and not partially)express his or her opinion. The color green may represent all his otherfilled out categories. Another mode of this exemplary embodiment isexhibiting multiple topics/categories that are connected in various waysto the one specific topic/category selected for exhibition. For example:parent topics for a category/topic, identical topics or categories to acategory/topic. In this mode only one topic might be selected forexhibition. In any mode, the cramped information exhibited to the readermay reside in the database and/or cache memory as illustrated in FIG. 7.With respect to FIG. 9, upon the user viewing one topic or more andtheir data, that may be constricted to a certain constriction ruleand/or filters, the user may also perform several actions like:

-   -   I. Selecting a list button (or the equivalent) titled “Add        Topic” that opens up a list of close proximity topics along the        hierarchy relation for selection, in order to add one of them to        the presentation on the exemplary embodiment, or the option to        be presented with a multiple topics' choice illustration and        choose a topic as shown in FIG. 14.    -   II. Selecting a button (or the equivalent) titled “Add/Update”        which presents the user with the illustration embodying        inserting new categories for a topic, or updating a specific        category, simultaneously introducing similar categories to his        text as shown in FIG. 13.        With respect to FIG. 9, upon selecting the checkboxes (or the        equivalent), another set of actions that the user is able to        perform on the categories/topics are:    -   I. Selecting a button (or the equivalent) titled        “Merge—equivalent text” which performs the same action called        merge that the singular category's button “!” performs, but on        multiple categories/topics.    -   II. Selecting a button (or the equivalent) titled        “Unify—identical topics different hierarchy” which is used for        topics/categories that have different text, but are actually        totally identical in their overall meaning. This situation can        happen in the system because their hierarchy line is assembled        differently, and so, their text may be missing or absolutely        different, but the whole meaning arriving from the combined        specific hierarchy line with the specific topics/categories text        of each of the topics/categories is identical. Execution of this        action by the user is similar to the merge action, despite not        having to decide which topic/category is the favorable one.    -   III. Selecting a button (or the equivalent) titled        “Delete—irrelevant” which performs an action of reducing the        relevance of the category to the topic in the system's        calculations, and also may perform the action of removing it        temporarily from the presentation or marking it as disabled with        an “X” or in a similar way, mainly for printing purposes.

FIG. 10 shows a display 1000 with an illustrative plurality ofcategories to be rated for importance by the user using a scale ofvalues, and inserted to the system as a listing, in accordance with someembodiments of the disclosed subject matter. The categories containingdata related to a topic for filling-in, and this is part of the “fillinga listing” process, the categories are rated for importance by the user,using a scale of values. While rating, the user may fill in descriptionfields' values attached to the categories for precise info; these willlater be summed up and aggregated for the main topic. Several subtopicsconnected to the main topic are laid out at the top of the card index,these subtopics may also be chosen by the user and then filled outpartly or completely by him or her. Additionally, some large textualfields without a scale of values may be permitted to be filled in. Thenall of these values and textual fields are inserted into the system asone listing belonging to the current user. The user may apply filters,search, sort and a general constriction of group/sphere to the datapresented that may reside in the database and/or cache memory asillustrated in FIG. 7. With respect to FIG. 10, upon the user filling inhis or her importance values to the categories he or she may bepresented with a separate window or another card index (or similar) thatcontains one or more large textual fields for filling in the story orbackground, these have less constraints than the text of the categorieswhen filled in, spelling checks for example are less abrasive.

FIGS. 11A and 11B include a display 1100 to show an illustrativeembodiment of a personal diary belonging to a specific topic, under aspecific hierarchy, in accordance with some embodiments. The largefields are exhibited when viewing a user's listing as shown in FIGS. 11Aand 11B. Before the user starts filling in his importance values in FIG.10, he or she may fill in the personal details of a person or thesituation related to the state he or she is describing, in a separatewindow or card index, or the similar. Including “Gender,” “Age,” and/oraccording to different spheres and groups also other details such as“Recovery,” “Therapy,” “Fate” related to a health sphere. Additionallythe user may provide a password for updating his listing in the futureand creating a personal diary as presented in FIGS. 11A and 11B. Thescale of values used in FIG. 10 may consist of three or more top levelsindicating that the user agrees with the text of the category, thedifference between those top levels is the ascending value indicatinghow important does the user think the category is to the maintopic/subtopic. The bottom of the scale may consist of two or morelevels indicating that the user partly agrees, or doesn't agree entirelywith text of the category in the context of the topic and subtopic. Incase the user choses a level indicting partial agreement with the text,he or she may be prompted to type in his or her own version of thecategory, one that he does agree with, or chose another category thatalready exists in the system for the same action. This revision may takeplace at the end of the “filling a listing” process or at middle of itas illustrated in further embodiments. In order to save the filled-indata to the database, there may be a minimum amount of categories thatmust be filled out for each of the main topic/subtopics. The minimumamount might be different for each sphere/group and/or topic/subtopic.Once the minimum amount and other requirements are met in the current“filling a listing” process, the data may be saved automatically in thebackground and updated according to the user's further fillings orupdates, nevertheless the user is allowed to cancel the save action byselecting a button (or the equivalent) titled “Cancel Save” next to theautomatic “Data Saved” notice.

FIGS. 11A and 11B show an illustrative embodiment of a personal diarybelonging to a specific topic under specific hierarchy, in accordancewith some embodiments of the disclosed subject matter. This diary mayinclude all of the specific writer's listings for that topic on thetimetable, and is to remain essentially the same as how the writercreated it and changed it along the way, only the writer is able tomodify and update his categories. Even though the diary is personal,other users may view it, and perform some actions on it. The diary mayalso present a reference to the system's aggregated info, compared withthe specific user's diary's info. For example, in the embodiment, thefield “% influence in topic” shows how dominant or/and popular or/andrelevant is the category for the topic within the listings. The field “%consent with values” shows how many users agreed with the listing'scategory's values. Topics' and categories' text which have similar otherfavored text in the system is shown in a proximate field marked with anarrow (or the similar) stating the system's favorable text. The datapresented may reside in the database and/or cache memory as illustratedin FIG. 7. The system acts on the assumption that updates relating tothe same state or time period of a specific listing is portrayed in thesame listing. The writer is prompted about modifying of categories'rates/description fields' values, and adding or subtracting categoriesthat are relating to the same state or time period to be in the samelisting. Update of these values and categories that refers to adifferent state or time is portrayed in a different listing.Furthermore, as long as a category's values don't change along thelistings' chain, it is still in effect, meaning the last values enteredare valid. With respect to FIGS. 11A and 11B, upon updating the diary,the writer may create a new listing by selecting the button (or theequivalent) “Update—New State.” Then he or she will be navigated to anew card index or the similar that is filled with all of the diary'scategories, including default values that are the most recent valueswithin the listings (non-relevant categories may be omitted). The writermay be prompted with a message that the new listing will encompass bydefault all of the categories enclosed in the previous listings andtheir last values in case they are not revised, and that only categoriesthat he will change will be part of the new listing. Other relevantcategories that aren't already included in the diary are also presentedamong the diary's categories. The process by which this new listing isinserted into the system is similar to the “filling a listing” processusing the same principles as the way it is inserted when it's notconnected to a diary, as shown in FIG. 10. Similar restrictions may beenforced and the user may change his personal details to match the newstate (and fill in the large textual fields). Other actions are alsoavailable within the embodiment presented in FIGS. 11A and 11B, by waysof selecting the button “!” at the appropriate field (or by similarways, like designated buttons). Actions on the whole diary may beactivated through the main topic and may include:

-   -   I. Creation of a new listing disconnected from the diary with        default categories and values that are the same as the diary's        categories, for users how wants to copy this specific data and        fill in their own values. This action is followed by the        “filling a listing” process as shown in FIG. 10.    -   II. Changing the diary's topic. The writer that created the        diary can chose to change the topic of the diary, by selecting        another topic form a list of topics as presented in FIG. 14. The        categories' values may be deducted from the current topic's        aggregation results, and calculated for aggregation within their        new topic. Other users can also chose to change the topic of a        diary but their changes won't take effect immediately, they may        be accumulated instead for future analysis. In any case, the        diary will always appear the same when the writer enters, any        changes will be noted in the presentation next to the original        writer's data.        Actions on categories or subtopics may be activated through the        button “!” at the appropriate field (or by similar ways, like        designated buttons) and may include:    -   I. Updating the text. The writer of the diary may be navigated        to a separate multiple categories/topics exhibition embodiment        as shown in FIG. 13.    -   II. Deleting a listing. The writer may delete a specific        listing.    -   III. Appealing against the connection between certain texts to        their favorable ones (which were chosen by the system). A        separate screen may be exhibited for entering a reason or by        similar ways.

FIG. 12 is a display 1200 illustrates a comprehensive topic's detailsexhibition according to some embodiments. This exhibition refers to themain topic both as a topic and as a category, and may contain the mostrelevant information in each of the related sections. Such as thesection titled “Listings Containing the Topic” which presents a shortsummary of listings in which the main topic is filled out as a category.Another section may be titled “Superior Topics” which presents parenttopics containing the main topic. Other section examples are shown inthe embodiment. Description fields may be titled “Average Values” or“Specific Values” when used for filtering. All the sections may beexpanded for exhibition by clicking the button (or the equivalent)titled “Show All” and being navigated to a multiple exhibitionsillustration as shown in FIG. 9. The user may also appeal against theconnection between the main topic and some of its acclaimed relatedtopics. The system enables the user to apply filters, search, sort andthe three general constrictions to the data presented that may reside inthe database and/or cache memory as illustrated in FIG. 7. Another setof sections comprising the topic's details may include:

-   -   I. “Listings Of The Topic” for presenting a short summary of        listings written by writers for the main topic.    -   II. “Subordinate Listings” for presenting categories or sub        topics that are written in listings for the topic, or associated        below it by other means.    -   III. “Summarized Topics—Identical By Unifying Or Merged        Hierarchy” for presenting all the topics that are identical to        the main topic, and notifying that these are the topics whose        values of importance and description fields are summarized and        exhibited throughout the system.    -   IV. “Merged Topics—Not Identical, Not Summarized” for presenting        all the topics that have similar text to the topic's text, but        their values are not summarized because they are not identical,        as they may have different hierarchy, meaning different        affiliation and context.        The sections may also include exhibiting the different        appearances of the main topic as a category with different        description fields' values combinations, under the section        titled “Choosing Specific Topic's Values.” According to the        identical topics constriction level, summarized and merged        topics are also presented with their values. A dedicated filter        may be placed near this section for choosing which values should        be presented and summed up for importance values. Another        section that may be exhibited is titled “Topics Marked As        Similar (remotely)—Not Merged, Not Unified” for presenting        topics/categories that the users have marked as identical or        have similar text, but the system hasn't authorized this        diagnosis yet, or may have even canceled this diagnosis.

FIG. 13 is a display 1300 that illustrates a category or sub-topic beingupdated or inserted in accordance with some embodiments. Validationchecks on the text may be performed by the system. Every change isassociated to the main topic under its hierarchy. The change, if it mayoccur, may be part of the current “filling a listing” process, anddocumented in the current listing, thereby allowing only the topic andsubtopics that are at the head of the listing to be the main presentedtopic at the header of the illustration for exhibition. The user mayapply search, sort and general constriction of spheres/groups to thedata presented, that may reside in the database and/or cache memory asillustrated in FIG. 7. Upon changing the categories/subtopics, theirtext, scale of values and description fields may be updated within the“Update Category Details” section. The system presents by default themain topic's and current listing's categories/subtopics. In FIG. 13 theyreside within the fields section at the bottom, for further filling inof importance rates. The description fields' values may be changed inthe update section or a separate window or the similar. Upon presentingthe topic's categories, before any text is changed or inserted in the“Update Category Details”, the user's categories are presented first,and may be highlighted with different colors same as performed in FIG.9. Optional colors are orange and green. Orange may represent partialagreement, a value for which the system may prompt the user to type inhis own text or choose another category from the system which he canfully and not partially express his opinion about. Changing a categoryrated as “Partly Agree” may delete it from the current listing andreplace it with the changed category. With respect to FIG. 13, when thetext of a category/subtopic is changed in the update section, similarcategories/subtopics to the new text are shown in the categories' fieldsbelow, for quick selection. Priority may be given tocategories/subtopics that are within the general constriction of thehierarchy relation levels titled “Direct Relation” and “Close Relation”to the main topic. Favorable text in the system may also be givenpriority. If categories/subtopics that have not been chosen as favorabletext (non-favorable) are more textually similar to the new text, theywill be exhibited first and may be followed by another category orsubtopic that was chosen as the favored text for them. After the text ischanged by the user he or she can't rate it as “don't agree” or “partlyagree,” selecting the button (or the equivalent) titled “Add” adds thenew text to the topic after validation checks, and exhibits it among thetopic's categories below. Marking the text as a “Sub Topic” mayeliminate the scale of values for it, making it a future card indexheader in the current listing. With respect to FIG. 13, new descriptionfields may be set while typing the categories' text. For example, withinthe constriction level of the “Generic” sphere, and also certain otherspheres/groups, every number typed in as a digit in the categories' textbecomes a description field (limited by max number of fields set by thesystem). These description fields may be titled “avg. first_”, “first_”,“second_”, or by other similar ways of marking the description fields'values within the text. Other spheres/groups may enforce a strictpre-defined set of optional description fields for certain topics, suchas “Repetition” and “Duration in days” in a health sphere of knowledge.

FIG. 14 is a display 1400 that illustrates a topic being selected forviewing or updating (or a topic being newly entered) according to someembodiments. Selection is either by ways of searching similar text tothe one being entered in the main topic's field at the header, or byways of navigating through the topics' trees of hierarchy for closerelation topics/categories. After a topic is selected it may bepresented or updated through other embodiments as illustrated in FIGS. 8and 10, along with its categories and subtopics. New topics are alsocreated through this embodiment under a specific hierarchy chosen by theuser. The user may apply search, sort and general constriction ofspheres/groups to the data presented, that may reside in the databaseand/or cache memory as illustrated in FIG. 7. While searching andpresenting topics/categories which are similar to the typed in maintopic, priority is given to favorable text in the system, still takinginto account better matches from non-favorable topics/categories, sameas described in FIG. 13. Selection from the trees of hierarchy is alsopossible, only topics and sub topics which have subordinate categoriesmay be shown in the trees, represented by their favorable text. That isuntil the moment a category is selected to be the main topic forselection, in this case it may be shown in the trees. With respect toFIG. 14, the illustration may include two trees for highlighting thedifference in terms of hierarchy connections, between the currentlychosen main topic at the header, and the proposed other topics forselection at the bottom. If a topic that already exists in the system ispresented in the main topic's field, it may also be presented at thebottom section, alongside with its subtopics and categories. But oncethe user changes the text at the main topic's field, the exhibition ischanged to show only similar topics/categories to the text, untilselection is made (and vice versa). Final selection of a topic to be thetransmitted for the desired action purposes, through the system's otherembodiments, is carried out by selecting the button (or the equivalent)titled “Select Topic” for topics that are already part of the system andunder a specific hierarchy. Topics that are not part of the system underthe specific hierarchy described in the “Topic's Hierarchy” field areentered into the system by selecting the button (or the equivalent)titled “New Topic In Hierarchy—Fill Listing,” validation checks on thetext may be performed by the system, followed by the “filling a listing”process for writing a complete listing for that new topic.

FIG. 15 illustrates a process to fill data (listing) associated with aspecific topic in accordance with some embodiments. First at S1510, awriter selects a topic at S1512 or identifies as the owner of a topic'sdiary at S1520 (based on the use of such embodiments as the exemplaryembodiments shown in FIGS. 14 and 11A/11B). These topics may reside in acomputer network based communities such as the one exemplified in thenetwork architecture shown in FIG. 7. Next, the writer may choose tofill in a new listing at S1514 and S1516, or update an existing listingfrom his diary at S1522, the listing includes rating several categoriesof the topic, and filling in description fields values for them (usingsuch embodiments as the exemplary embodiments shown in FIGS. 9 and 10)at S1518 or S1524. Adding new categories to the listing is alsoavailable through such embodiment as the exemplary embodiment shown inFIG. 13. Optionally, in a new listing the writer may fill in personaldetails and password for future updates and diary management. Next atS1526, the writer is prompted to revise categories that he rated aspartly agreeing with (e.g., at 51530 and S1532), this prompt may befollowed by an update category embodiment as illustrated in FIG. 13.

Through this embodiment he or she may optionally type in new text forthe category which must coincide with certain validation checks insteadof the category he or she partially agrees with. Alternatively (andoptionally), he or she may choose another category from the ones alreadyresiding in the system, that has a text that he does agree withaltogether, and not partially. Finally the writer may fill in longtextual fields containing his story, background and other fieldsdepending on the sphere or group he or she is a part of. These areentered within a separate window, or another card index (or the similar)in the rating illustration such as described in FIG. 10. With respect tothe process described in FIG. 15, once the minimum amount of categoriesand other requirements are met, in the current “filling a listing”process, the data may be saved to the database automatically in thebackground and updated according to the writer's further fillings orupdates. Nevertheless the writer is able to “cancel” the save action byselecting a button (or the equivalent) that may be titled “Cancel Save.”When the filling a listing process is finished, meeting all therequirements and performing the save action. Then all of the ratings,values and textual fields may be inserted into the system as one listingbelonging to the current writer, and aggregated with the system's data.

FIG. 16 is a high level view of a system 1600 architecture according tosome embodiments. The system may include an accumulated information datastore 1610 that includes a plurality of topic nodes, each topic nodehaving a text description of limited length and at least some of thetopic nodes being associated with one or more attributes (e.g., a ratingscore, a description field, etc.). Note that a particular topic node maybe associated as a parent topic node to one or more other child topicnodes such that the topic nodes in the accumulated information datastore form at least one data tree. The system may further include aninformation processing engine 1620 coupled to the accumulatedinformation data store 1610 and programmed to access information in theaccumulated information data store 1610. The information processingengine 1620 may determine that a plurality of topic node textdescriptions are similar and classify them as similar topic nodes. Theinformation processing engine 1620 may also select at least a part ofthe text description associated with one of the similar topic nodes as afavorable text description for the similar topic nodes. According tosome embodiments, the information processing engine 1620 may alsoautomatically identify all other topic nodes in the accumulatedinformation data store 1610 that have the same text description as oneof the similar topic nodes and merge all of the other identified topicnodes with the similar topic nodes. The information processing engine1620 may also unify the similar topic nodes as identical topic nodeswhen they are currently grouped together as having the same upper treehierarchy and classify the unified topic nodes that are also similartopic nodes as a single topic node (represented by the favorable textdescription) and any attribute associated with the unified topic nodesmay be automatically mathematically combined. According to someembodiments, the system 1600 further includes a user interface platform1630 programmed to receive from a user (e.g., from a user device 1640) aselection of a topic node as a current topic node of interest. The userinterface platform 1630 may then display to the user information aboutthe current topic node of interest, including information about thechild topic nodes of the current topic node of interest. According tosome embodiments, the child topic nodes might be summarized: (1) firstby similarity between child topic nodes which are reduced in amount andrepresented by favorable text descriptions, with mathematically combinedattributes, and (2) second by the unification of identical topic nodesto the current topic node of interest which increases the amount ofchild topic nodes being displayed by the coupling process.

FIG. 17 is a method 1700 according to some embodiments. Note that thesteps of FIG. 17 may be performed via an appropriate hardware and/or beexecuted in any order that is applicable. At S1710, the system maystore, in an accumulated information data store, a plurality of topicnodes, each topic node having a text description of limited length andat least some of the topic nodes being associated with one or more“attributes”. As used herein, the term “attribute” might refer to anyinformation associated with a topic node. Examples of attributes mightinclude, for example, a rating score reflecting an importance of a topicnode, a description field having pre-determined values that describe anaspect of a topic node, a popularity value, a date value, etc. Accordingto some embodiments, a particular topic node is associated as a parenttopic node to one or more other child topic nodes such that the topicnodes in the accumulated information data store form at least one datatree. At S1720, the system may access information in the accumulatedinformation data store and determine that a plurality of topic node textdescriptions are similar and classifying them as similar topic nodes atS1730. The similarly of text might be pointed out by a user or beautomatically determined by the system (e.g., taking into accountsynonyms, abbreviations, nicknames, acronyms, and other semantic rules).At S1740, the system may select at least part of the text descriptionassociated with one of the similar topic nodes as a favorable textdescription for the similar topic nodes. At S1750, the system mayautomatically identify all other topic nodes in the accumulatedinformation data store that have the same text description as one of thesimilar topic nodes and merge all of the other identified topic nodeswith the similar topic nodes, wherein the similar topic nodes and all ofthe other identified topic nodes are now classified as similar to eachother and retain their associated attributes. At S1760, the system mayunify the similar topic nodes as identical topic nodes when they arecurrently grouped together as having the same upper tree hierarchy. AtS1770, the system may classify the unified topic nodes that are alsosimilar topic nodes as a single topic node represented by the favorabletext description, wherein any attributes associated with the unifiedtopic nodes is automatically “mathematically combined”. Note thatattributes might be mathematically combined in any of a number ofdifferent ways. Examples of mathematical combinations might beassociated with an averaging process, a summing process, a selection ofa minimum value, a selection of a maximum value, a selection of anearliest date, a selection of a latest date, etc.

The embodiments described herein may be implemented using any number ofdifferent hardware configurations. For example, FIG. 18 is a blockdiagram of an apparatus or platform 1800 that may be, for example,associated with the system 700 of FIG. 7 and/or the system 1600 of FIG.16. The apparatus or platform 1800 comprises a processor 1810, such asone or more commercially available Central Processing Units (“CPUs”) inthe form of one-chip microprocessors, coupled to a communication device1820 configured to communicate via a communication network (not shown inFIG. 18). The communication device 1820 may be used to communicate, forexample, with one or more remote reader devices, writer devices, etc.The apparatus or platform 1800 further includes an input device 1840(e.g., a computer mouse and/or keyboard to input database and/or systeminformation) and/an output device 1850 (e.g., a computer monitor torender a display, provide alerts, transmit recommendations, and/orcreate reports). According to some embodiments, a mobile device,database system, and/or PC may be used to exchange information with theapparatus 1800.

The processor 1810 also communicates with a storage device 1830. Thestorage device 1830 may comprise any appropriate information storagedevice, including combinations of magnetic storage devices (e.g., a harddisk drive), optical storage devices, mobile telephones, and/orsemiconductor memory devices. The storage device 1830 stores a program1812 and/or an information processing engine 1814 for controlling theprocessor 1810. The processor 1810 performs instructions of the programs1812, 1814, and thereby operates in accordance with any of theembodiments described herein. For example, the processor 1810 may accessan accumulated information data store that includes a plurality of topicnodes, each topic node having a text description of limited length andat least some of the topic nodes being associated with at least oneattribute. In some cases, a particular topic node may be associated as aparent topic node to one or more other child topic nodes such that thetopic nodes in the accumulated information data store form at least onedata tree. The processor 1810 may then determine that a plurality oftopic node text descriptions are similar and classify them as similartopic nodes. At least part of text description associated with one ofthe similar topic nodes may be selected as a favorable text descriptionfor the similar topic nodes. The processor 1810 may automaticallyidentify all other topic nodes in the accumulated information data storethat have the same text description to the similar topic nodes and mergeall of the other identified topic nodes with the similar topic nodes,wherein the similar topic nodes and all of the other identified topicnodes are now classified as similar to each other and retain theirassociated attributes. The processor 1810 may also unify the similartopic nodes as identical topic nodes when they are currently groupedtogether as having the same upper tree hierarchy. The processor 1810 mayclassify the unified topic nodes that are also similar topic nodes as asingle topic node represented by the favorable text description, whereinany attribute associated with the unified topic nodes is automaticallymathematically combined.

The programs 1812, 1814 may be stored in a compressed, uncompiled and/orencrypted format. The programs 1812, 1814 may furthermore include otherprogram elements, such as an operating system, clipboard application, adatabase management system, and/or device drivers used by the processor1810 to interface with peripheral devices. As used herein, informationmay be “received” by or “transmitted” to, for example: (i) the anapparatus or platform 1800 from another device; or (ii) a softwareapplication or module within the apparatus or platform 1800 from anothersoftware application, module, or any other source.

In some embodiments (such as the one shown in FIG. 18), the storagedevice 1830 further stores an accumulated information database 1900,reader data 1860, and/or writer data 1870. An example of a database thatmay be used in connection with the an apparatus or platform 1800 willnow be described in detail with respect to FIG. 19. Note that thedatabase described herein is only an example, and additional and/ordifferent information may be stored therein. Moreover, various databasesmight be split or combined in accordance with any of the embodimentsdescribed herein.

Referring to FIG. 19, a table is shown that represents the accumulatedinformation database 1900 that may be stored at the apparatus 1800according to some embodiments. The table may include, for example,entries identifying topic nodes associated with tree hierarchies. Thetable may also define fields 1902, 1904, 1906, 1908, 1910, 1912, 1914,1916, 1918 for each of the entries. The fields 1902, 1904, 1906, 1908,1910, 1912, 1914, 1916, 1918 may, according to some embodiments,specify: a topic node identifier 1902, a text description 1904, a rating1906, children 1908, unified nodes 1910, similar nodes 1912, a parentnode 1914, averaged descriptions 1916, and spheres/groups 1918. Theaccumulated information database 1900 may be created and updated, forexample, based on information from reader and/or writer devices.

The topic node 1902 may be, for example, a unique alphanumeric codeidentifying a topic node within a tree hierarchy (e.g., and mightrepresent a label for that particular node). The text description 1904might comprise an alphanumeric string of limited length that may beassociated with the topic node. The rating 1906 might comprise, forexample, a numerical value that has been entered by a user (or a groupof users) to rate the importance of that particular topic node. Thechildren 1908 might represent labels or topic node identifiers 1902 thatcreate a tree hierarchy. The unified nodes 1910 might represent labelsor topic node identifiers 1902 that indicate identical topic nodes thathave been unified in accordance with any of the embodiments describedherein (e.g. with nodes ABC and Z being unified as illustrated in FIG.19). The similar nodes 1912 might indicate nodes that a user hasidentified as being associated with a single topic because of semanticsimilarity (and may share a favorable text) (e.g., with nodes AC and ZCbeing merged as illustrated in FIG. 19). The parent node 1914 mightindicate the node's immediate parent in the tree hierarchy. The averageddescriptions 1916 might be similar to the rating 1904 but describe otherdetails that can be averaged about the node other than the node'sgeneral importance. The spheres/groups 1918 might indicate areas ofknowledge or groups of users associated with the topic node.

The following illustrates various additional embodiments of theinvention. These do not constitute a definition of all possibleembodiments, and those skilled in the art will understand that thepresent invention is applicable to many other embodiments. Further,although the following embodiments are briefly described for clarity,those skilled in the art will understand how to make any changes, ifnecessary, to the above-described apparatus and methods to accommodatethese and other embodiments and applications.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a,” “an,” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising” (or the similar), when used in this specification,specify the presence of stated features, integers, steps, operations,elements, and/or components, but do not preclude the presence oraddition of one or more other features, integers, steps, operations,elements, components, and/or groups thereof. It should also be notedthat, in some alternative implementations, the functions noted in theblock of a figure may occur out of the order noted in the figures. Forexample, two blocks shown in succession may, in fact, be executedsubstantially concurrently, or the blocks may sometimes be executed inthe reverse order, depending upon the functionality involved.

Glossary

This section will set forth some terms used in this patent applicationalong with their associated meanings. As used herein, the phrase “treeof topics” may refer to, for example, a hierarchical arrangement of datamade out of short textual phrases/paragraphs, aligned in a branchedtree, wherein each node is called a “topic.” One topic is at the head ofthe tree and it can have endless chains of children topics. Note that atopic in a tree may be dynamically shown as the main presented topic atthe header of a presentation, or a category with a rating relating toits presented parent topic. For example, when presenting node “A,”another node “AC” may be referred to as its category (as illustrated inFIG. 6). When presenting node “AC”, “AC” may be referred to as the mainpresented topic and node “ACE” may be presented as its child topic. Asused herein, the term “topic” may refer to, for example, every node in atree that represents certain textual data. This may include, forexample, nodes that temporarily are referred to as a “child topic,” a“category,” a “sub-topic,” a “main presented topic,” etc. According tosome embodiments, any topic node may have one or more child topic nodes.Moreover, a topic node may include values such as a rating anddescription fields.

As used herein, the terms “main presented topic, “presented topic,” and“main topic” may refer to, for example, a topic that in the temporaryperspective view of the system that is set to act as the main topic forwhich data is shown or aggregated. According to some embodiments, thefocus is on the main topic and all the averages and ratings are relatingto it. The main topic may, for example, usually be presented at theheader of presentation screens.

As used herein, the term “child topic” may refer to, for example, atopic that in the temporary perspective view of the system is treated asthe child of its parent topic (relating to it) in the tree of topics.For example, child topics may be associated with situations where thesystem presents the subordinate children of the main presented topic.Note that a child topic may have a rating value, in which case it may bereferred to as a “category.” If the child topic does not have a ratingvalue, it may be referred to as a “sub-topic.” That is, a “sub-topic”might represent a child topic that has no rating. This type of node maytypically point out a more specific segment of its parent topic (e.g.,body→hand). Note that a sub-topic may usually have one or more childtopics (e.g., “ABC” may be a sub-topic of “AB” while “AB” is sub topicof “A”). As used herein, the term “category” may refer to, for example,a child topic that has a rating relating to its parent topic. Forexample, “ABCX” and “ABCZ” may be categories of “ABC” (as illustrated inFIG. 6).

As used herein, the term “sphere” may refer to, for example, differentspheres of information, which represent different fields of knowledge,such as “Sport,” “Health,” “Vehicles,” etc. These spheres contain allthe trees of topics in a system. The spheres are closed environments ofinformation which might or might not share information with each other,by ways of that might include averaged ratings and/or selected favorabletext, and/or coupled child topics, etc. These spheres may behierarchically arranged, the highest level may be referred to as the“Generic” sphere which is the most abstract (and contains all the datafrom the other spheres). The spheres are also closed environments ofrules such as openness to change. The data within a sphere may be veryopen to changes, which dynamically builds trees of content andconnections between them. Note that the data within a sphere mightinstead be strict and preloaded in advance to use of the system, perhapsonly letting users fill-in a categories' rating. For example, a “Health”sphere may be preloaded with all the medical conditions, with sub topicsof “Symptoms,” “Causes,” and “Actions.” As used herein, the term “group”may refer to, for example, a structure similar to a sphere but definedby different groups of people instead of different fields of knowledge.

As used herein, the term “similar” (associated with a “merge” action)may be associated with similarity between topics that is suggested bythe users in various screens of the system (merge action), in whichseveral topics are shown together. Note that similar topic nodes aresometimes referred to as “equivalent” topic nodes. Also note thatsimilarity may only refer to the semantic text of the topics withoutconsidering parent topics. According to some embodiments, the system maydecide to eventually declare two topics as similar, such as when arelatively large number of users point out similarity (or by otherstatistical comparison). The system may also automatically decidesimilarity between topics with no user intervention. As used herein, thephrase “favorable text or topic” may refer to, for example, situationswhere two or more topics are declared as similar, and the text of one ofthem (the favorable node) is chosen to represent them all in the systemor different favorable text within each specific sphere/group.

As used herein, the term “identical” (associated with a “unified”action) may refer to, for example, a situation wherein a unifyingoperation is suggested by the users in various screens of the system(merge action), in which several topics are shown together. Note thatidentical topic nodes are sometimes referred to as “paired” topic nodes.Unification may refer to the semantic text of the topics and theiraffiliation considering their line of parent topics' semantics, so thatas a whole combined with the texts of each of their parent's lines theyare considered identical. The system may decide to eventually declaretwo topics as identical if a relatively large number of users point outunification (or by other statistical comparison). Note that the systemmay also automatically decide unification between topics with no userintervention (e.g., where there is a continuous line of respectivesimilar topics to the top or to the same parent, or to unified parents).

As used herein, the terms “summarized” or “averaged” may refer to, forexample, ratings that are calculated together. Note that the term“averaged” is sometimes referred to as “calculated together.” Forexample, topics that become unified may be summarized. That is, when oneof them is shown as a child topic, the calculations that determine itsrating, popularity, description values, etc. may be averaged with thetopics that are summarized with it. When one of them is shown as themain presented topic, all of the children connected to the othersummarized topics may be shown as his child topics. As used herein, theterm “coupled” may refer to, for example, children of unified topics.Such topics may be shown together for one of the unified topics andsimilar topics that are coupled may become unified.

As used herein, the phrases “selection field” and “constriction field”may refer to, for example, the three dimensions by which the system'sdata may be divided: “identical topics,” “hierarchy relation,” and/or“sphere/group.” These may set the way the system is calculated, forexample for deciding which child topics to display for the mainpresented topic, and with what values. Note that the term “identicaltopics level” is sometimes referred to as “singularity level”. They canbe set automatically by the system differently for every topic, group,and/or sphere (and may also be changed by users). According to someembodiments, the default setting is: showing and calculating the directchild topics of all the unified topics with the main presented topic, inthe current group/sphere.

The present invention has been described in terms of several embodimentssolely for the purpose of illustration. Persons skilled in the art willrecognize from this description that the invention is not limited to theembodiments described, but may be practiced with modifications andalterations limited only by the spirit and scope of the appended claims.

1. A system to summarize textual content, by way of hierarchicalrelations between texts, comprising: an accumulated information datastore, including: a plurality of topic nodes, each topic node having atext description of limited length, wherein a particular topic node isassociated as a parent topic node to one or more other child topic nodessuch that the topic nodes in the accumulated information data store format least one data tree; and an information processing engine,comprising: a computer processor; and a memory storage device includinginstructions that when executed by the computer processor enable thesystem to: (i) access information in the accumulated information datastore, (ii) determine that a plurality of topic node text descriptionsare similar and classifying them as similar topic nodes, (iii)automatically identify topic nodes in the accumulated information datastore that have the same text description as one of the similar topicnodes and merge the identified topic nodes with the similar topic nodes,wherein the similar topic nodes and the identified topic nodes are nowclassified as similar to each other, (iv) unify the similar topic nodesas identical topic nodes when they are currently grouped together ashaving the same upper tree hierarchy, and (v) couple together all childtopic nodes of identical topic nodes to become child topic nodes of eachof the identical topic nodes, wherein coupled child topic nodes that aresimilar are also unified and become identical.
 2. The system of claim 1,wherein texts contain from a single word to a paragraph.
 3. The systemof claim 1, wherein the topic nodes are associated with metadatacomprising at least one or more attributes.
 4. The system of claim 3,wherein at least some of the metadata associated with unified topicnodes is automatically mathematically combined.
 5. The system of claim3, wherein the information processing engine is further programmed toselect at least a part of the text description associated with one ofthe similar topic nodes as a favorable text description for the similartopic nodes, to represent them in the system.
 6. The system of claim 3,wherein execution of the instructions by the computer processor of theinformation processing engine further enable the system to: determinethat a plurality of topic nodes which are not similar, but have textdescriptions that, when combined with each of their upper treehierarchy's text descriptions, form a similar combination, compriseidentical topic nodes, unify the non-similar identical topic nodes, as aresult of unification, at least some of the metadata of the non-similar,identical topic nodes is automatically mathematically combined, and as aresult of unification of non-similar topic nodes, couple together allchild topic nodes of identical topic nodes to become child topic nodesof each of the identical topic nodes, wherein coupled child topic nodesthat are similar are also unified.
 7. The system of claim 3, whereinexecution of the instructions by the computer processor of theinformation processing engine further enable the system to: dynamicallyunify the similar topic nodes as identical topic nodes when they arecurrently grouped together as having the same upper tree hierarchy andun-unify them to become similar but not identical when they are not. 8.A system, comprising: an accumulated information data store, including:a plurality of topic nodes, each topic node having a text description oflimited length and at least some of the topic nodes being associatedwith one or more attributes, wherein a particular topic node isassociated as a parent topic node to one or more other child topic nodessuch that the topic nodes in the accumulated information data store format least one data tree; and an information processing engine,comprising: a computer processor; and a memory storage device includinginstructions that when executed by the computer processor enable thesystem to: (i) access information in the accumulated information datastore, (ii) determine that a plurality of topic node text descriptionsare similar and classifying them as similar topic nodes, (iii) select atleast a part of the text description associated with one of the similartopic nodes as a favorable text description for the similar topic nodes,(iv) automatically identify all other topic nodes in the accumulatedinformation data store that have the same text description as one of thesimilar topic nodes and merge all of the other identified topic nodeswith the similar topic nodes, wherein the similar topic nodes and all ofthe other identified topic nodes are now classified as similar to eachother and retain their associated attributes, (v) unify the similartopic nodes as identical topic nodes when they are currently groupedtogether as having the same upper tree hierarchy, and (vi) classify theunified topic nodes that are also similar topic nodes as a single topicnode represented by the favorable text description, wherein anyattribute associated with the unified topic nodes is automaticallymathematically combined.
 9. The system of claim 8, wherein texts containfrom a single word to a paragraph.
 10. The system of claim 9, wherein atleast some of the attributes are associated with at least one of: (i) arating score reflecting an importance of a topic node, (ii) adescription field having pre-determined values that describe an aspectof a topic node, (iii) a popularity value, and (iv) a date value. 11.The system of claim 9, wherein the mathematical combination ofattributes for unified topic nodes is associated with at least one of:(i) an averaging process, (ii) a summing process, (iii) a selection of aminimum value, (iv) a selection of a maximum value, (v) a selection ofan earliest date, and (vi) a selection of a latest date.
 12. The systemof claim 9, wherein the information processing engine is furtherprogrammed to automatically determine unification of similar topic nodeswith the same upper tree hierarchy when each of the parent lines of thesimilar topic nodes are similar until: (i) a shared parent node isreached, (ii) identical parent nodes are reached, or (iii) the head ofeach tree is reached.
 13. The system of claim 9, wherein the informationprocessing engine is further programmed to classify information aboutchild topics of similar but not identical parent topic nodes to one ofthe parent topic nodes, as if they were the parent topic node's childtopic nodes, temporarily making these children currently coupled withthe original parent topic's children.
 14. A method to summarize textualcontent, by way of hierarchical relations between texts, comprising:accessing, by a computer processor of an information processing engine,data in an accumulated information data store, the accumulatedinformation data store including a plurality of topic nodes, each topicnode having a text description of limited length, wherein a particulartopic node is associated as a parent topic node to one or more otherchild topic nodes such that the topic nodes in the accumulatedinformation data store form at least one data tree; determining that aplurality of topic node text descriptions are similar and classifyingthem as similar topic nodes; automatically identifying topic nodes inthe accumulated information data store that have the same textdescription as one of the similar topic nodes and merge the identifiedtopic nodes with the similar topic nodes, wherein the similar topicnodes and the identified topic nodes are now classified as similar toeach other; unifying the similar topic nodes as identical topic nodeswhen they are currently grouped together as having the same upper treehierarchy; determining that a plurality of topic nodes which are notsimilar, but have text descriptions that, when combined with each oftheir upper tree hierarchy's text descriptions, form a similarcombination, comprise identical topic nodes; and unifying thenon-similar identical topic nodes.
 15. The method of claim 14, whereintexts contain from a single word to a paragraph.
 16. The method of claim14, wherein the topic nodes are associated with metadata which includesat least one or more attributes.
 17. The method of claim 16, wherein atleast some of the metadata associated with unified topic nodes isautomatically mathematically combined.
 18. The method of claim 14,wherein the information processing engine is further programmed toselect at least a part of the text description associated with one ofthe similar topic nodes as a favorable text description for the similartopic nodes, to represent them in the system.
 19. The method of claim14, wherein as a result of unification of both similar and non-similartopic nodes, couple together all child topic nodes of identical topicnodes to become child topic nodes of each of the identical topic nodes,wherein coupled child topic nodes that are similar are also unified andbecome identical.
 20. The method of claim 14, wherein the informationprocessing engine is further programmed to dynamically unify the similartopic nodes as identical topic nodes when they are currently groupedtogether as having the same upper tree hierarchy and un-unify them tobecome similar but not identical when they are not.
 21. Anon-transitory, computer-readable medium having executable instructionsstored therein, the medium comprising instructions associated with amethod to summarize textual content, by way of hierarchical relationsbetween texts, comprising: accessing, by a computer processor of aninformation processing engine, data in an accumulated information datastore, the accumulated information data store including a plurality oftopic nodes, each topic node having a text description of limitedlength, wherein a particular topic node is associated as a parent topicnode to one or more other child topic nodes such that the topic nodes inthe accumulated information data store form at least one data tree;determining that a plurality of topic node text descriptions are similarand classifying them as similar topic nodes; automatically identifyingtopic nodes in the accumulated information data store that have the sametext description as one of the similar topic nodes and merge theidentified topic nodes with the similar topic nodes, wherein the similartopic nodes and the identified topic nodes are now classified as similarto each other; unifying the similar topic nodes as identical topic nodeswhen they are currently grouped together as having the same upper treehierarchy; determining that a plurality of topic nodes which are notsimilar, but have text descriptions that, when combined with each oftheir upper tree hierarchy's text descriptions, form a similarcombination, comprise identical topic nodes; and unifying thenon-similar identical topic nodes.
 22. The medium of claim 21, whereintexts contain from a single word to a paragraph.
 23. The medium of claim21, wherein the topic nodes are associated with metadata which includesat least one or more attributes.